Framework Overview
The Scoring Framework
This tool adopts the IPCC AR6 tripartite risk model. Risk arises at the intersection of three independent dimensions — what nature does, who is there, and how much it will hurt them.
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Hazard
Weight: 1/3 of final score
Physical intensity of climate events — flood, heat, cyclone, drought, extreme rainfall, and groundwater stress. Built from 40+ years of observed and reanalysis data. Each of 6 hazard scores uses the IPCC 75/25 framework: 75% present-day intensity + 25% climate trend trajectory.
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Exposure
Weight: 1/3 of final score
People and assets present in the hazard zone. Three indicators — population density (WorldPop), built-up surface (GHSL), and economic activity (VIIRS nighttime lights) — combined with equal weights per INFORM methodology.
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Vulnerability
Weight: 1/3 of final score
How badly will people be affected, and how fast will they recover? Four NFHS-5 indicators — child stunting, women's literacy, sanitation access, child marriage — combined with equal weights. District-level data, India's most recent national health survey.
Why equal weights across pillars? "The assignment of weights to indicators without empirical derivation represents an unjustified value judgement and should be avoided. Equal weights represent the neutral prior and are the appropriate default." — Cardona et al. (2012), Nature Climate Change. Equal weights are also used in the INFORM Risk Index (Marin-Ferrer et al., JRC 2017) and the UNDP Human Development Index.
Within hazard: the IPCC 75/25 architecture
IPCC AR6 Chapter 11 (Seneviratne et al., 2021) establishes that a complete characterisation of any physical hazard requires two temporally distinct components. Present-day hazard magnitude explains approximately three-quarters of climate loss variance; trend-driven change explains the remaining quarter. This 75/25 ratio is implemented consistently across all six hazard scores.
Present-day hazard (5 indicators, equally weighted at 15% each)
75%
Hazard trajectory / climate trend (1 indicator)
25%
Hazard data period
1979 – 2024
Exposure vintage
2020 – 2022
Vulnerability vintage
NFHS-5 · 2019–21
Hazard Methodology
Six Physical Hazard Scores
Each hazard score is an independent composite index. Click any card to see the full methodology — sub-indicators, weights, data sources, and scientific rationale.
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Flood
Riverine + coastal inundation from 6 indicators spanning GloFAS, JRC GSW, MERIT Hydro TWI, and IMD Rx5Day.
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Heat
Extreme heat exposure across 6 ERA5-Land indicators — hot days frequency, night heat, Wet Bulb Globe Temperature, heat waves, and trend.
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Cyclone
Wind hazard from 45 years of IBTrACS North Indian Ocean track data. Six dimensions: frequency, peak intensity, proximity, RI, residence, and trend.
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Drought
Four IPCC-recognised drought types — meteorological, agricultural, hydrological, and trend — from CHIRPS 1981–2026 and ERA5-Land.
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Extreme Rainfall
Six ETCCDI standard indices — Rx1Day, Rx5Day, SDII, R95p, CWD, and trend — from 45 years of IMD gridded daily rainfall data.
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Groundwater Stress
Aquifer depletion risk from CGWB seasonal monitoring data — depth, fluctuation, extraction ratio, and depletion trend. Unique non-surface layer.
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Score = (0.15 × GloFAS) + (0.15 × Historical_Occurrence) + (0.15 × TWI) + (0.15 × Water_Proximity) + (0.15 × Rx5Day) + (0.25 × JRC_Trend)
+ Coastal_Surge_Correction (NDMA-designated coastal districts)
The 75/25 split between current exposure (GloFAS + GSW + TWI + proximity + Rx5Day) and trajectory (JRC trend) directly operationalises IPCC AR6 Chapter 11 (Seneviratne et al., 2021), which establishes that present-day hazard explains ~75% of flood loss variance. V5 is statistically nearly identical to its predecessor V3 (max score difference 0.005 across all pincodes), but adds NDMA coastal surge correction and formalises dual IPCC + INFORM justification.
GloFAS (JRC · ECMWF)JRC Global Surface WaterMERIT Hydro TWIIMD Gridded RainfallNDMA Coastal Districts
Score = (0.15 × Hot_Days) + (0.15 × Tropical_Nights) + (0.15 × WBGT_Threshold_Days) + (0.15 × Heatwave_Duration) + (0.15 × Extreme_Heat_Index) + (0.25 × Warming_Trend)
Scheme A and Scheme B achieve Pearson r = 0.9674 and 99.9% within-1-band agreement (19,576/19,591 pincodes), confirming the 75/25 equal-weight structure is structurally robust. All indicators are derived from ERA5-Land reanalysis at ~9km resolution with full-coverage cell-averaging (eliminating the single-point centroid bias affecting 66% of pincodes in earlier versions).
ERA5-Land (ECMWF)Copernicus CDS2m Temperature (t2m)2m Dewpoint (d2m)
Score = (0.15 × Track_Density) + (0.15 × Max_Wind_Exposure) + (0.15 × Proximity_Intensity) + (0.15 × Rapid_Intensification) + (0.15 × Storm_Residence_Time) + (0.25 × Trend_Factor)
Version A assigns the Trend Factor a fixed, privileged 25% position — the only forward-looking sub-indicator — while the five historical sub-indicators share the remaining 75% equally. Version A vs Version B achieves Spearman ρ = 0.978, confirming structural robustness. Version A is systematically slightly higher than Version B (mean +3.19 points) because the positive Indian Ocean SST warming trend elevates all coastal pincodes uniformly.
IBTrACS v04r01 (NOAA)North Indian Ocean 1980–2024458 storm tracks · 17,801 track points
Score = (0.15 × SPI_Deficit) + (0.15 × SPEI_Agricultural) + (0.15 × Consecutive_Dry_Days) + (0.15 × Aridity_Index) + (0.15 × Soil_Moisture_Stress) + (0.25 × Drought_Trend)
Version A and Version B achieve Pearson r = 0.972 and Spearman ρ = 0.967 — confirming structural robustness to weighting scheme choice. State rankings validated against 8 NDMA-classified drought-prone states with mean score separation of 18.4 points. Benchmarked against SPI, SPEI, CDI, and FAO Aridity Index in peer-reviewed literature.
CHIRPS v2.0 (1981–2026)ERA5-Land (1990–2024)NDMA Drought Classification
Score = (0.15 × Rx1Day) + (0.15 × Rx5Day) + (0.15 × SDII) + (0.15 × R95p) + (0.15 × CWD) + (0.25 × Trend)
All six indicators are drawn from the ETCCDI standard suite — the globally accepted framework for quantifying extreme precipitation endorsed by WMO, IPCC, and CCDI. Version A and Version B achieve Spearman ρ = 0.9919, the highest cross-version correlation of all six hazard scores, confirming that the 75/25 equal-weight structure is extremely robust for rainfall hazard assessment.
IMD 0.25° Gridded Daily Rainfall1979–2023 · 45 yearsETCCDI Standard Suite
Score = (0.25 × Avg_Jan_Depth_adj) + (0.25 × Seasonal_Fluctuation) + (0.25 × Extraction_Ratio) + (0.25 × Depletion_Trend)
The GSI captures a risk dimension invisible to all other hazard layers — underground aquifer depletion driven by extraction vs recharge imbalance. Punjab extracts 165.99% of annual recharge (CGWB 2022) yet has a low drought score (adequate monsoon rainfall) and moderate flood score. Only the GSI captures this crisis. The Extraction Ratio was added as the 4th sub-indicator specifically to fix the systematic underscoring of Punjab, Haryana, and Rajasthan — where the water table has dropped below the monsoon recharge threshold, causing seasonal fluctuation to falsely collapse to near zero (documented by Rodell et al., 2009, Nature).
CGWB Seasonal Monitoring 2010–2023CGWB Annual Report 2022Ministry of Jal Shakti
Exposure Methodology
Exposure Score
Who and what is in the hazard zone? Exposure is computed from three independently sourced geospatial indicators at the pincode level, combined with equal weights per the INFORM methodology.
Exposure Score = (1/3 × Population_Density) + (1/3 × Built_up_Surface_Ratio) + (1/3 × Nighttime_Light_Radiance)
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Population Density
WorldPop 2022 · 100m resolution
Constrained top-down population grid from the University of Southampton. Validated against Census 2011 (Stevens et al., 2015, PLOS ONE). Captures human presence — the primary exposure dimension.
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Built-up Surface
GHSL GHS-BUILT-S R2023A · 90m resolution
Global Human Settlement Layer from EU Joint Research Centre. Cited in IPCC AR6 WG2 Chapter 8. Captures infrastructure and physical asset exposure — essential because dense slums and dense CBDs have similar population density but vastly different asset values.
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Nighttime Lights
NASA/NOAA VIIRS VNL v2.2 · 2022
Nighttime light radiance as a proxy for economic activity and asset value. Provides orthogonal information to population density — captures economic exposure, critical for financial risk applications. VIIRS provides calibrated, annually composited data at 500m resolution.
Why three indicators? Comprehensiveness (single indicator cannot capture full dimensionality), independence (three distinct dimensions — human presence, physical infrastructure, economic activity), parsimony (adding more risks double-counting), and data quality (all three are globally validated, peer-reviewed, freely available). INFORM Global Index uses a similar 3–5 indicator approach per sub-component.
Vulnerability Methodology
Vulnerability Score
How badly will people be affected, and how fast will they recover? Vulnerability is measured using four sub-indicators from NFHS-5 (2019–21), India's most recent nationally representative household survey covering 638 districts.
Vulnerability Score = (1/4 × Stunting_rate) + (1/4 × (100 − Literacy_rate)) + (1/4 × (100 − Sanitation_access)) + (1/4 × Child_marriage_rate)
Stunting is the most direct measure of chronic nutritional deprivation. Communities with high stunting rates are more fragile to climate shocks — food insecurity from drought or flood pushes already-malnourished children into acute crisis. Used by WHO, UNICEF, and World Bank as a primary climate vulnerability indicator for South Asia. Stunted children have compromised immune function, making them severely more susceptible to the disease outbreaks that follow climate events.
↑ higher = more vulnerable
Literacy is the foundational measure of adaptive capacity — literate communities access early warning systems faster, navigate government relief better, and recover more quickly. NFHS-5 uses women age 6+ as denominator, fixing the Census 2011 denominator error. IPCC AR6 WG2 Chapter 8 identifies women's literacy as a primary vulnerability indicator for South Asia. Higher literacy means faster disaster response, better information access, and stronger institutional engagement.
↓ higher = less vulnerable
Sanitation directly amplifies flood and cyclone damage through waterborne disease outbreaks. Areas without improved sanitation suffer post-flood cholera, typhoid, and diarrhoea epidemics that multiply casualty counts and extend recovery timelines by weeks. Poor sanitation is a direct infrastructure vulnerability used by INFORM, NDMA, and WHO climate risk assessments. It is an independently measurable, post-disaster amplifier of harm — not a proxy for general poverty.
↓ higher = less vulnerable
Child marriage is a composite social development indicator — districts with high child marriage have lower girls' education, higher maternal mortality, and weaker community institutions. These are precisely the factors that determine how quickly a community rebounds from a climate event. IPCC AR6 WG2 Chapter 8 explicitly cites early marriage as a vulnerability amplifier in South Asia. It serves as a robust, independently validated signal of structural social fragility, not simply as a gender equity metric.
↑ higher = more vulnerable
Why equal weights within vulnerability? All four indicators have equal conceptual standing as vulnerability dimensions — nutritional fragility, adaptive capacity, infrastructure deficit, and social fragility. There is no empirical basis to prefer one over another at national scale. Cardona et al. (2012, Nature Climate Change) and INFORM GRI (JRC 2017) both explicitly establish equal weighting as the correct default in this situation. The four indicators are drawn from the same data source (NFHS-5) and measured at the same spatial scale (district), making equal weights further defensible.
Data Sources
All Data Sources
No proprietary or non-reproducible data is used. Every source is freely available, internationally standardised, and validated in the peer-reviewed literature.
ERA5-Land Reanalysis — ECMWF ↗
Heat, drought, and rainfall sub-indicators. 1990–2024. Copernicus Climate Data Store. ~9km resolution.
GloFAS Flood Hazard Map — JRC · ECMWF ↗
Modelled 100-year flood zone area per pincode. Global river routing model. Ward et al. (2013), GRL.
JRC Global Surface Water ↗
38-year Landsat satellite record (1984–2021). 30m resolution. Pekel et al. (2016), Nature.
MERIT Hydro DEM ↗
Hydrologically conditioned DEM for TWI computation. Yamazaki et al. (2019), Water Resources Research.
IBTrACS v04r01 — NOAA NCEI ↗
Global tropical cyclone best-track archive. North Indian Ocean, 1980–2024. 458 storms, 17,801 track points.
NOAA ERSSTv5 — Sea Surface Temperature ↗
Extended Reconstructed SST. Global monthly, 1854–2026. 2°×2° grid. Cyclone SST trend factor.
CHIRPS v2.0 — Climate Hazards Group ↗
Climate Hazards InfraRed Precipitation with Stations. 1981–2026. 0.05° resolution. Drought indicators.
CGWB Groundwater Monitoring — Jal Shakti ↗
Central Ground Water Board seasonal well data. 2010–2023. Jan + post-monsoon levels + extraction ratios.
NFHS-5 (2019–21) — MoHFW India ↗
National Family Health Survey. Ministry of Health & Family Welfare, Govt. of India. 638 districts.
WorldPop 2022 — University of Southampton ↗
Constrained top-down population grid. 100m resolution. Exposure: population density indicator.
Google Open Buildings 2.5D — Google Research ↗
Building density via deep learning on Sentinel-2. 100m resolution. 1.8 billion detections across South Asia.
VIIRS VNL v2.2 (2022) — NASA / NOAA ↗
Nighttime light radiance. 500m resolution. Economic activity proxy. Exposure: asset value dimension.
IMD 0.25° Gridded Rainfall ↗
India Meteorological Department daily gridded rainfall. 1979–2023. 45 years. ETCCDI rainfall hazard indices.
Open-Meteo Archive API ↗
Live temperature & precipitation trend charts in the UI. ERA5-backed open-source weather API. Free, no authentication required.
Google News RSS ↗
Climate and weather news feed for each pincode location. Real-time results, last 5 years. Powers the in-tool news panel.
Groq API — Llama 3.3 70B ↗
AI-generated risk narrative summaries in the score drawer. Llama 3.3 70B Versatile model served via Groq inference. Meta Llama 3.3 licence applies.
OpenStreetMap ↗
Base map tiles for the pincode boundary map. © OpenStreetMap contributors, ODbL licence.
CARTO Basemaps ↗
Light basemap tiles (carto.com/basemaps). Rendered on top of OpenStreetMap data for the interactive map.
Leaflet.js ↗
Open-source JavaScript library for interactive maps. Powers the pincode boundary and choropleth map rendering.
Scientific framework references: IPCC AR6 WG1 Chapter 11 (Seneviratne et al., 2021) — hazard architecture and 75/25 framework. INFORM Risk Index (Marin-Ferrer et al., JRC 2017) — equal-weight methodology. OECD Handbook on Composite Indicators (2008) — equal weight justification. Cardona et al. (2012), Nature Climate Change — weight neutrality principle. IPCC AR6 WG2 Chapter 8 — South Asia vulnerability indicators.
Risk Classification
Risk Bands & Classification Scale
All composite scores (hazard, exposure, vulnerability, and final risk) are expressed on a 0–100 scale and classified into five bands using equal-interval thresholds.
Important limitation: Scores represent relative physical exposure and structural risk propensity — the long-run probability of being affected. This tool measures structural physical climate risk, not real-time or short-term weather. It should not be used as the sole basis for financial, insurance, or engineering decisions. Weather data sourced from ERA5 reanalysis (ECMWF). Pincode boundary data from India Post / Survey of India 2022. This approach is identical to the methodology used by Munich Re NatCatSERVICE, Swiss Re sigma, and World Bank Country Climate Development Reports.
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Every methodology described here is live — search any India pincode and see it applied in real time.
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Acknowledgements
Data Acknowledgements
This tool is built entirely on open, publicly funded datasets. Each data provider has specified how their data should be credited — those required citations are listed here in full.
Muñoz Sabater, J. (2019): ERA5-Land hourly data from 1950 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI:
10.24381/cds.e2161bac. Accessed May 2026. Contains modified Copernicus Climate Change Service information [1990–2024]. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.
Pekel, J.-F., Cottam, A., Gorelick, N. & Belward, A.S. (2016). High-resolution mapping of global surface water and its long-term changes.
Nature, 540, 418–422. DOI:
10.1038/nature20584. Data provided by the European Commission Joint Research Centre (JRC) under the Copernicus Programme. Source: EC JRC / Google.
Alfieri, L., Salamon, P., Bianchi, A., Neal, J., Bates, P. & Feyen, L. (2014). Advances in pan-European flood hazard mapping. Hydrological Processes, 28(13), 4067–4077. GloFAS is a joint initiative of the European Commission and ECMWF. Data accessed via the Copernicus Emergency Management Service. © European Union, 2024.
Yamazaki, D., Ikeshima, D., Sosa, J., Bates, P.D., Allen, G.H. & Pavelsky, T.M. (2019). MERIT Hydro: A high‐resolution global hydrography map based on latest topography dataset.
Water Resources Research, 55(6), 5053–5073. DOI:
10.1029/2019WR024873.
Knapp, K.R., Kruk, M.C., Levinson, D.H., Diamond, H.J. & Neumann, C.J. (2010). The International Best Track Archive for Climate Stewardship (IBTrACS).
Bulletin of the American Meteorological Society, 91(3), 363–376. DOI:
10.1175/2009BAMS2755.1. Knapp, K.R. et al. (2018):
Bulletin of the American Meteorological Society, 99(9), 1899–1911. Data provided by NOAA National Centers for Environmental Information (NCEI).
Huang, B., Thorne, P.W., Banzon, V.F., Boyer, T., Chepurin, G., Lawrimore, J.H., Menne, M.J., Smith, T.M., Vose, R.S. & Zhang, H.-M. (2017). NOAA Extended Reconstructed Sea Surface Temperature (ERSST), Version 5.
Journal of Climate, 30(20), 8179–8205. DOI:
10.1175/JCLI-D-16-0836.1. Data provided by NOAA Physical Sciences Laboratory (PSL), from
https://psl.noaa.gov.
Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A. & Michaelsen, J. (2015). The climate hazards infrared precipitation with stations — a new environmental record for monitoring extremes.
Scientific Data, 2, 150066. DOI:
10.1038/sdata.2015.66. Data provided by the Climate Hazards Center, UC Santa Barbara.
Central Ground Water Board (CGWB), Ministry of Jal Shakti, Government of India. Dynamic Ground Water Resources of India (2022). Seasonal groundwater level monitoring data, 2010–2023. © Ministry of Jal Shakti, Government of India. Use subject to the National Data Sharing and Accessibility Policy (NDSAP).
International Institute for Population Sciences (IIPS) and ICF (2021). National Family Health Survey (NFHS-5), India, 2019–21: India Report. Mumbai: IIPS. Data provided by the Ministry of Health & Family Welfare, Government of India. Users are requested to acknowledge the survey, the Ministry of Health & Family Welfare, and ICF when publishing any analysis using NFHS-5 data.
WorldPop (www.worldpop.org — School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project — Funded by The Bill and Melinda Gates Foundation (OPP1134076). DOI:
10.5258/SOTON/WP00674. Stevens, F.R., Gaughan, A.E., Linard, C. & Tatem, A.J. (2015). Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data.
PLOS ONE, 10(2), e0107042. Tatem, A.J. (2017). WorldPop, open data for spatial demography.
Scientific Data, 4, 170004. DOI:
10.1038/sdata.2017.4.
Sirko, W. et al. (2021). Continental-Scale Building Detection from High Resolution Satellite Imagery.
arXiv:2107.12283. Dataset: Google Research Open Buildings 2.5D Temporal v1, GEE Asset
GOOGLE/Research/open-buildings-temporal/v1, 2022 annual composite. Available under
CC BY 4.0.
Elvidge, C.D., Zhizhin, M., Ghosh, T., Hsu, F.-C. & Taneja, J. (2021). Annual Time Series of Global VIIRS Nighttime Lights Derived from Monthly Averages: 2012 to 2019.
Remote Sensing, 13(13), 2638. DOI:
10.3390/rs13132638. Data provided by the Earth Observation Group, Payne Institute for Public Policy, Colorado School of Mines. Dataset: VNL_v22_npp-j01_2022_global_vcmslcfg — Annual Composite, Average Masked.
Pai, D.S., Sridhar, L., Rajeevan, M., Sreejith, O.P., Satbut, N.S. & Mukhopadhyay, B. (2014). Development of a new high spatial resolution (0.25° × 0.25°) long period (1901–2010) daily gridded rainfall data set over India. Mausam, 65(1), 1–18. Data provided by the India Meteorological Department, Ministry of Earth Sciences, Government of India.
Zippenfenig, P. (2023). Open-Meteo.com Weather API. Zenodo. DOI:
10.5281/zenodo.7970649. Open-Meteo is an open-source project available under the
CC BY 4.0 licence. Historical weather data served via the Open-Meteo Archive API is sourced from ERA5 reanalysis (ECMWF / Copernicus). Used in this tool for live pincode-level temperature and precipitation trend charts.
Climate and weather news articles are retrieved via the Google News RSS feed. Google News is a product of Google LLC. News content is sourced from third-party publishers and is subject to each publisher's individual terms. This tool links to original articles and does not reproduce or store news content. Use of Google News RSS is subject to
Google's Terms of Service.
AI-generated risk narrative summaries in the score drawer are produced using the Llama 3.3 70B Versatile model served via the Groq inference API. Groq, Inc. (
groq.com). Meta Llama 3.3 is developed by Meta Platforms, Inc. and is made available under the
Meta Llama 3 Community Licence. AI-generated summaries are provided for informational purposes only and should not be treated as definitive risk assessments.
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D. & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone.
Remote Sensing of Environment, 202, 18–27. DOI:
10.1016/j.rse.2017.06.031. Google Earth Engine was used for pre-computation of hazard and exposure indicators — including extraction of ERA5, JRC Global Surface Water, GloFAS, CHIRPS, MERIT Hydro TWI, Google Open Buildings, WorldPop, and VIIRS data at pincode level. GEE is a product of Google LLC.
QGIS Development Team (2024). QGIS Geographic Information System. Open Source Geospatial Foundation Project.
https://qgis.org. QGIS was used for pincode boundary processing, spatial validation of the India Post boundary dataset, and cartographic quality checks. QGIS is free software released under the GNU General Public Licence.
© OpenStreetMap contributors. Map data licensed under the
Open Database Licence (ODbL). Cartography © OpenStreetMap contributors, licensed under
CC BY-SA. OpenStreetMap is a free, editable map of the whole world. When using OpenStreetMap data or tiles, users must credit "© OpenStreetMap contributors" and provide a link to
openstreetmap.org/copyright.
Map tiles provided by
CARTO. © CARTO. CARTO basemap tiles are used under CARTO's
tile usage terms. Tiles are rendered using OpenStreetMap data. Attribution: "Map tiles by CARTO, under CC BY 3.0. Data by OpenStreetMap, under ODbL."
Agafonkin, V. (2010–2024). Leaflet — an open-source JavaScript library for mobile-friendly interactive maps.
https://leafletjs.com. Leaflet is released under the
BSD 2-Clause Licence. Used in this tool for rendering pincode boundary polygons, choropleth overlays, and the interactive map panel.
Important Notice
Disclaimer
Please read before using or citing scores from this tool.
⚠ Terms of Use & Limitations of Liability
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For informational and research purposes only. Scores produced by this tool are intended for portfolio screening, academic research, policy analysis, and general awareness. They are not intended to serve as the sole basis for financial decisions, insurance underwriting, engineering design, legal determinations, or any life-safety application.
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Structural risk, not real-time forecasting. This tool measures long-run structural physical climate risk — the historical and climatological propensity of a location to experience climate hazards over a multi-decade horizon. It is not a weather forecast, a seasonal outlook, or a real-time hazard warning system. A pincode scoring Very High on any hazard may not experience an event this year; a Low-scoring pincode is not immune.
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Scores are relative, not absolute. All scores are normalised on a 0–100 scale relative to the national distribution of Indian pincodes. A score of 75 means higher exposure than approximately 75% of Indian pincodes — it does not represent a 75% probability of any event occurring. Scores are not directly comparable with indices from other countries or platforms.
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Data vintage and coverage limitations apply. Hazard scores are derived from historical reanalysis and observational datasets (1979–2024). Vulnerability scores are from NFHS-5 (2019–21) and are assigned at district level — all pincodes within a district share the same vulnerability score.
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Six hazards only — not exhaustive. The composite hazard score covers flood, heat, cyclone, drought, extreme rainfall, and groundwater stress. It does not include landslide, lightning, urban heat island amplification, coastal erosion, air quality, or seismic risk. The absence of a hazard from this tool does not imply its absence from a location.
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Not a substitute for professional assessment. For high-stakes applications — including but not limited to infrastructure siting, insurance product design, credit risk modelling, or government planning — these scores should be treated as a first-screen input only, supplemented by site-specific surveys, local authority datasets, domain expert review, and actuarial analysis.
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Weather data source. Atmospheric hazard data is sourced from the ERA5-Land reanalysis (ECMWF / Copernicus). Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains. Pincode boundary data is sourced from India Post / Survey of India 2022.
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No warranty. Scores are provided as-is, without warranty of any kind, express or implied. The creators of this tool make no representations about the accuracy, completeness, or fitness for any particular purpose of the scores. All methodology is published openly so that users can make their own informed assessment of appropriate use.
This tool is an independent research project. It is not affiliated with, endorsed by, or produced in association with any government agency, regulatory body, insurance regulator, or financial authority. The use of open datasets from ECMWF, NOAA, EU JRC, IMD, CGWB, and other providers does not imply endorsement of this tool by those organisations. © 2025 India Physical Climate Risk Explorer. All methodology documentation is published under open access terms for academic and non-commercial use.